Systematic tissue annotations of genomics samples by modeling unstructured metadata

Nathaniel T. Hawkins, Marc Maldaver, Anna Yannakopoulos, Lindsay A. Guare and Arjun Krishnan ()
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Nathaniel T. Hawkins: Michigan State University
Marc Maldaver: Michigan State University
Anna Yannakopoulos: Michigan State University
Lindsay A. Guare: Michigan State University
Arjun Krishnan: Michigan State University

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract There are currently >1.3 million human –omics samples that are publicly available. This valuable resource remains acutely underused because discovering particular samples from this ever-growing data collection remains a significant challenge. The major impediment is that sample attributes are routinely described using varied terminologies written in unstructured natural language. We propose a natural-language-processing-based machine learning approach (NLP-ML) to infer tissue and cell-type annotations for genomics samples based only on their free-text metadata. NLP-ML works by creating numerical representations of sample descriptions and using these representations as features in a supervised learning classifier that predicts tissue/cell-type terms. Our approach significantly outperforms an advanced graph-based reasoning annotation method (MetaSRA) and a baseline exact string matching method (TAGGER). Model similarities between related tissues demonstrate that NLP-ML models capture biologically-meaningful signals in text. Additionally, these models correctly classify tissue-associated biological processes and diseases based on their text descriptions alone. NLP-ML models are nearly as accurate as models based on gene-expression profiles in predicting sample tissue annotations but have the distinct capability to classify samples irrespective of the genomics experiment type based on their text metadata. Python NLP-ML prediction code and trained tissue models are available at https://github.com/krishnanlab/txt2onto .

Date: 2022
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DOI: 10.1038/s41467-022-34435-x

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